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11 results for “CO2 fertilization”
CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia: Dataset
<p>This repository is linked to the paper "CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia" submitted to Annals of Forest Science and written by Louis DE WERGIFOSSE (corresponding author), Frédéric ANDRE, Hugues GOOSSE, Steven CALUWAERTS, Lesley DE CRUZ, Rozemien DE TROCH, Bert VAN SCHAEYBROECK and Mathieu JONARD.</p> <p>The files stored in the repository are the input files that should be used in the model HETEROFOR to retrieve the results displayed in the study and the corresponding results themselves. The source code of the model HETEROFOR can be freely accessed and downloaded (https://doi.org/10.5281/zenodo.3591348). Additional information on the model can be found in the following description papers: Jonard et al., 2020 (https://doi.org/10.5194/gmd-13-905-2020) and de Wergifosse et al., 2020 (https://doi.org/10.5194/gmd-13-1459-2020).</p> <p>The repository contains three directories. The first (HETEROFOR_input_files) comprises the additional files to those in the model repository presented in the previous paragraph needed to run the model for the purpose of this study. The second directory (Simulation_outputs_raw) contains the data directly provided by the model without any processing. The third directory (Simulation_outputs_raw) includes the model outputs after processing.</p> <p>The directory "HETEROFOR_input_files" is constituted of two directories called "Climate_files" and "Stand_files". "Climate_files" is subdivided in three sub-directories. Sub-directory "Original_downscaled_CORDEX_timeseries" contains the climate projections of the four sites and scenarios described in the study. These downscaled timeseries have been produced by the Royal Meteorological Institute of Belgium under the program CORDEX.be, which is part of EURO-CORDEX. A bias correction has been further applied to these climate timeseries that are stored in the "Bias_corrected_timeseries" sub-directory. The files of these two sub-directories should be used in HETEROFOR as "Meteorological data" input files. The "CO2_concentrations" sub-directory includes the yearly averaged projected concentrations for the three RCP scenarios described in the paper. In HETEROFOR, they should be put as input in the "Atmospheric CO2 concentration" part after selecting the option "Variable over time". The second directory called "Stand files" contain the six inventory files described in the study for which a thinning has been applied. They should be used in HETEROFOR as "Inventory data" input files.</p> <p>The directory "Simulation_outputs_raw" is divided similarly to the study into two simulation experiments. The "First simulation experiment" directory is further subdivided into constant and time-dependent CO2 concentrations like in the study and contains one file for the regular modality and one for the thinning modality. All the files are constructed the same way with, for each tree and site (or stand, soil and climate), annual values of Net Primary Production (NPP) in kg of carbon, transpiration and potential transpiration in L under the different climate scenarios. In addition, the "Phenology" directory contains, for each day and under all climate scenarios, the green proportion (proportion of green leaves comprised between 0 and 1) for the two tree species considered in the study (Common oak and European beech).</p> <p>Finally, the directory "Simulation_outputs_processed" is constructed similarly to "Simulation_outputs_raw" but all the data are integrated in one file at the yearly time step. However, the units change with the NPP expressed in gC/m2 and transpiration and potential transpiration in mm (or L/m2) while the vegetation period is averaged according to the percentage of species occurrence<br> in the different stands.</p> <p><br> For more information concerning this repository or the study, please do not hesitate to contact Louis DE WERGIFOSSE (louis.dewergifosse@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
CEDAR-GPP: A Spatiotemporally Upscaled Dataset of Gross Primary Productivity Incorporating CO2 Fertilization
<p>Overview:<br>----------<br>CEDAR-GPP is a global Gross Primary Productivity (GPP) data product, including monthly GPP estimates at 0.05º spatial resolution. These datasets were generated via upscaling eddy covariance measurements with machine learning and satellite data. CEDAR-GPP uniquely incorporated the direct CO2 fertilization effect (CFE) using both data-driven and theoretical approaches. GPP estimates were produced from ten different model setups that vary by temporal span, direct CFE incorporation method, and GPP partitioning approaches. CEDAR stands for ups<strong>C</strong>aling <strong>E</strong>cosystem <strong>D</strong>ynamics with <strong>AR</strong>tificial intelligence.</p> <p>CEDAR-GPP consists of GPP estimates from ten model setups, differing by temporal range, methods for quantifying CO2 fertilization effects, and the partitioning methods used to derive GPP from eddy covariance measurements. Users are encouraged to refer to the user manual for a structured approach to selecting the most appropriate dataset.</p> <p> </p> <p>Authors:<br>----------<br>Yanghui Kang, Maoya Bassiouni, Max Gaber, Xinchen Lu, Trevor Keenan</p> <p> </p> <p>File Structure:<br>----------<br>Each zip file contains GPP data from a CEDAR model setup.</p> <p> </p> <p>File Naming Convention:<br>----------<br>All netCDF files follow this naming convention:<br>CEDAR-GPP_<version>_<model-setup>_<YYYYMM>.nc</p> <p>Where:<br><model-setup> comprises of <temporal_span>_<CFE_option>_<GPP_partitioning><br><temporal_span>: ST denotes short-term (2001 to 2020); LT denotes long-term (1982 to 2020)<br><CFE_option>: 'Baseline' indicates no direct CO2 fertilization effect, 'CFE-ML' represents direct CO2 fertilization incorporated by ML, 'CFE-Hybrid' implies direct CO2 fertilization incorporated by theory<br><GPP_partitioning>: 'NT' for night-time GPP partitioning method, 'DT' for day-time GPP partitioning method</p> <p><br>NetCDF characteristics:<br>----------<br>- Spatial Resolution: 0.05 degree<br>- Temporal Resolution: Monthly<br>- Temporal Coverage: Short-term (ST): 2001-2020; Long-term (LT): 1982 - 2020<br>- Image Dimension: Rows: 3600, Columns: 7200<br>- Units: gCm^-2day^-1<br>- Fill Value: -9999<br>- Multiply By Scale Factor: 0.01<br>- Data Type: uint16<br>- File Size: Approximately 99 MB per file</p> <p><br>Data variables:<br>----------<br>- GPP_mean: monthly gross primary productivity (gCm^-2day^-1), mean from 30 model ensemble<br>- GPP_std: standard deviation of 30 model ensemble</p> <p><br>Support Contact:<br>----------<br>For any queries related to this dataset, please contact:</p> <p>Name: Yanghui Kang<br>Email: kangyanghui@gmail.com</p> <p> </p> <p> </p>
Soil and understory CO2 respiration, CH4, and N2O fluxes, tree biomass and litter, and soil carbon stock after a long-term N fertilization of a Scots pine forest in Finland
<p>Data of forest soil respiration, soil and undestory respiration, CH4, and N2O fluxes, soil temperature and volumetric water content (Data_Karstula_GHG_temp.swc.csv), continuous soil temperature and moisture data (Data_Karstula_measured_temperature_2021_2023.csv, Data_Karstula_measured_moisture_2021_2023.csv), forest biomass and litter (Data_Karstula_total_biomass_litter.csv, Data_Karstula_measured_litter_2021_2023.csv), and soil C stocks (Data_Karstula_soc.csv) from the boreal Scots pine forest site Karstula after a long-term N fertilization in Finland (62°54'43.343"N; 24°34'16.021"E).</p> <p>The dataset is used for the publication "Tupek et al. : <strong>Lower sensitivity of microbial respiration to soil moisture after long-term N fertilization increases soil carbon retention in a Scots pine forest</strong>. 2024".</p>
N2O and CO2 fluxes and related environmental conditions in response to manure and synthetic fertilization treatments, with and without a urease inhibitor
<p>Agricultural best management practices (BMPs) intended to solve one environmental challenge may have unintended climate impacts. For example, manure injection is often promoted for its potential to reduce runoff and N loss as NH<sub>3</sub>, but the practice has been shown to increase N<sub>2</sub>O, a powerful GHG, compared to surface application. Urease inhibitor application with N fertilizer is another BMP that can enhance N retention by reducing NH<sub>3</sub> emissions, but its impact on N<sub>2</sub>O emissions is mixed. Thus, we measured N<sub>2</sub>O, CO<sub>2</sub>, soil mineral N availability, soil moisture, soil temperature, and yield in a two-year perennial hayfield trial with four fertilization treatments (manure injection, manure broadcast, synthetic urea, and control) applied with or without a urease inhibitor in Alburgh, VT (n=4 treatment replicates; 32 total subplots). This dataset includes the daily N<sub>2</sub>O and CO<sub>2</sub> fluxes in every subplot on each sampling day in our trial, along with cumulative emissions for 2020 and 2021. We also include the other variables that we measured (listed above) and relevant weather data (precipitation and air temperature). Note that during the second treatment application in 2021, the incorrect treatment was applied to one synthetic urea subplot. We therefore excluded this subplot from our analysis of daily fluxes after 30 July 2021 (n=3 synthetic urea replicates). Details about our sampling methods are available in the corresponding manuscript.</p>
Estimates of soil nutrient limitation on the CO2 fertilization effect for tropical vegetation
<p>Data for CO2 fertilization experiments and CMIP6 model simulations used in publication: "Estimates of soil nutrient limitation on the CO2 fertilization effect for tropical vegetation"</p>
Data from: Carbon isotope trends across a century of herbarium specimens suggest CO2 fertilization of C4 grasses
<p>Increasing atmospheric CO<sub>2</sub> is changing the dynamics of tropical savanna vegetation. C<sub>3</sub> trees and grasses are known to experience CO<sub>2</sub> fertilization, whereas responses to CO<sub>2</sub> by C<sub>4</sub> grasses are more ambiguous. Here, we sample stable carbon isotope trends in herbarium collections of South African C<sub>4</sub> and C<sub>3</sub> grasses to reconstruct <sup>13</sup>C discrimination. We found that C<sub>3</sub> grasses showed no trends in <sup>13</sup>C discrimination over the past century but that C<sub>4</sub> grasses increased their <sup>13</sup>C discrimination through time, especially since 1950. These changes were most strongly linked to changes in atmospheric CO<sub>2</sub> rather than to trends in rainfall climatology or temperature. Combined with previously published evidence that grass biomass has increased in C<sub>4</sub>-dominated savannas, these trends suggest that increasing water use efficiency due to CO<sub>2</sub> fertilization may be changing C<sub>4</sub> plant-water relations. CO<sub>2</sub> fertilization of C<sub>4</sub> grasses may thus be a neglected pathway for anthropogenic global change in tropical savanna ecosystems.</p>
Improving gross primary production estimation accuracy on the Qinghai-Tibet Plateau considering the effect of atmospheric CO2 fertilization
<p>This GPP dataset was generated by the improved GPP estimation model which introduced atmospheric CO<sub>2</sub> fertilization effect and canopy-to-leaf CO<sub>2</sub> concentration gradients into the CASA model. The dataset was provided in TIF format at a month interval. The valid value ranges from 0 to 1000, and the background filled value is set to NoData. The scale factor of the data is 1. Each TIF file represents a month GPP at a daily cumulative value (unit: g C m<sup>-2</sup> month<sup>-1</sup>).</p>
Data from: Carbon isotope trends across a century of herbarium specimens suggest CO2 fertilization of C4 grasses
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N2O and CO2 fluxes and related environmental conditions in response to manure and synthetic fertilization treatments, with and without a urease inhibitor
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Trajectories and metadata for Bach et al.: 'Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO2 removal'
<p>Trajectory and metadata files from a Southern Ocean surface particle release experiment using Connectivity Modeling System (Paris et al. 2013, <a href="https://github.com/beatrixparis/connectivity-modeling-system">https://github.com/beatrixparis/connectivity-modeling-system</a>) run offline in the MOM01 model (Stewart et al., 2017; Spence et al., 2017; Morrison et al., 2020), a global 0.1° ocean sea-ice model, based on version 5 of the Modular Ocean Model (MOM) code (mom-ocean.github.io) (Griffies, 2012).</p> <p> Here particle trajectories are are split into 28 netcdf files (numbered traj_file_01.nc through traj_file_28.nc), and uploaded altogether as a single .tar.gz compressed file. Metadata files are also uploaded containing information on the CMS setup and release locations.</p> <p>Netcdf files contain particle trajectory positions, along with temperature, salinity, status in or out of the mixed layer, release date, and particle exit status (see <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a> for details on trajectory files). Other CMS setup files included in each release directory are nest_1.nml, runconf.list, ibm.list, and releaseFile_matchtransport. These input files contain information needed to reproduce the experiment using the MOM01 model output, and are explained in detail in the <a href="https://github.com/beatrixparis/connectivity-modeling-system/blob/master/User-Guide-v2.pdf">CMS user guide</a>.</p> <p> </p> <p>Citation of associated paper: Bach, L., V. Tamsitt, K. Baldry, J. McGee, E. Laurenceau-Cornec, R. Strzepek, Y. Xie, P. W. Boyd, 2021. Holistic re-evaluation of Southern Ocean Iron Fertilization for atmospheric CO<sub>2</sub> removal, <em>submitted </em></p> <p> </p> <p>References:</p> <p>Griffies, S. M. (2012). Elements of the modular ocean model (MOM). <em>GFDL Ocean Group Tech. Rep</em>, 7(620), 47.</p> <p> Morrison, A. K., Hogg, A. M., England, M. H., & Spence, P. (2020). Warm Circumpolar Deep Water transport towards Antarctic driven by local dense water export in canyons. <em>Science Advances</em>, 6(18), eaav2516.</p> <p> Paris, C. B., Helgers, J., van Sebille, E., & Srinivasan, A. (2013). Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean. <em>Environmental Modelling and Software</em>, 42, 47-54.</p> <p>Spence, P., Holmes, R. M., Hogg, A. M., Gries, S. M., Stewart, K. D., & England, M. H. (2017). Localized rapid warming of West Antarctic subsurface waters by remote winds. <em>Nature Climate Change</em>, 7(8), 595.</p> <p>Stewart, K., Hogg, A. M., Gries, S., Heerdegen, A., Ward, M., Spence, P., & England, M. H. (2017). Vertical resolution of baroclinic modes in global ocean models. <em>Ocean Modelling</em>, 113, 50-65.</p>
Supporting data for the Eastern equatorial Pacific dust fertilization and source weathering influences on the Eocene to Miocene CO2 decline
<p>This is supporting data for Equatorial Pacific dust fertilization and source weathering influences on Eocene to Miocene global CO<sub>2 </sub>decline.</p>
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Allen Brain Atlas
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